Parallel simulation of stochastic Petri nets using spatial decomposition

The authors address the problem of developing parallel simulation techniques to analyze stochastic Petri net (SPN) models. The approach of parallel simulation is to divide a general SPN spatially into several connected subnets. The rich and complex structure of Petri nets necessitates the development of an algorithm which can handle general forms of network partitions. The authors present an algorithm based on the time warp strategy for optimistic parallel simulation. The various subnetworks are simulated in parallel by several logical processes (LPs) which synchronize by rollbacks. Each LP simulates a subnetwork and advances its local simulation time as far as it can. When a token is needed by other subnetworks, a message with the local simulation time (called token time) will be sent to the proper LPs. When a simulation error is detected in a LP, such as receiving a message with a small token time, the LP will roll back to the simulation time indicated by the token time of the received message and then resimulate the firing process from that point on